混合注意力优化的SAR图像小目标检测方法

doi:10.12305/j.issn.1001-506X.2025.08.10

Abstract

Abstract:

In recent years, convolutional neural network （CNN） achieves remarkable success in synthetic aperture radar （SAR） image ship detection. However, there are still considerable challenges in detecting small targets. Regarding this, an improved detection network based on you only look once （YOLO） v5, combining the spatial-aware channel attention （SCA）, self-attention mechanism, and contextual feature fusion （CFF） strategy to enhance the detection performance of small ships. Firstly, SCA improves detection accuracy by suppressing background information and highlighting target-related features. Secondly, self-attention module is introduced in the backbone network and detection lager of YOLOv5 to capture global information and enhance localization capabilities. Finally, by fusing shallow and deep features, the compensates for the loss of small target information during feature extraction, further improving detection accuracy. Experimental results based on the LS-SSDDv1.0 dataset show that the improved network achieves a mean average precision （mAP） 0.5 of 78.9%, significantly outperforming existing methods.

Key words: synthetic aperture radar (SAR) image, ship detection, attention mechanism, feature fusion, small target detection

CLC Number:

TP 391.4

Weihong FU, Wenhong PENG, Naian LIU. SAR image small target detection method with hybrid attention optimization[J]. Systems Engineering and Electronics, 2025, 47(8): 2519-2526.

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References 30

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SCA	C3TR	TPH	CON3	@AP0.5	@AP0.5:0.95
—	—	—	—	74.5	28.4
√	—	—	—	77.4	29.7

SCA	C3TR	TPH	CON3	@AP0.5	@AP0.5:0.95
—	—	—	—	74.5	28.4
—	√	—	—	76.7	28.6
—	—	√	—	78.0	29.1

SCA	C3TR	TPH	CON3	@AP0.5	@AP0.5:0.95
—	—	—	—	74.5	28.4
—	—	—	√	75.8	28.8

SCA	C3TR	TPH	CON3	@AP0.5	@AP0.5:0.95
—	—	—	—	74.5	28.4
√	√	—	—	77.7	29.4
√	—	√	—	78.6	29.2
√	√	—	√	78.3	29.7
√	√	√	√	78.9	30.0

网络结构	P	R	@AP0.5	@AP0.5:0.95
Faster R-CNN^[25]	73.8	77.7	74.8	—
Double-Head R-CNN^[26]	74.4	78.5	75.7	—
SII-Net^[27]	79.3	68.2	76.1	—
SAR-Net^[6]	58.4	82.0	76.2	—
YOLOv4^[28]	48.2	80.6	72.8	—
YOLOv5m	82.8	67.2	74.5	28.4
YOLOv7^[29]	81.0	64.1	71.2	26.7
YOLOX^[30]	66.0	77.4	74.0	—
YOLOv8m	83.5	66.7	76.0	30.6
YOLOX+CMA+SSJC^[9]	70.4	80.1	77.2	—
本文方法	85.5	71.4	78.9	30.0

SAR image small target detection method with hybrid attention optimization

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